Last updated: 2022-03-29

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Knit directory: S:/KJP_Biolabor/Projects/scSeq_Hefendehl/

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#get sample data
samples.integrated@meta.data %>% as.data.frame() -> samplemeta

# convert to correct data type 

# define genotype as is
samplemeta$Genotype_corr = factor(samplemeta$Genotype=="wt", levels=c(F,T), labels = c("APPPS1+", "WT"))
samplemeta$Genotype_corr = relevel(samplemeta$Genotype_corr, ref="WT")

samplemeta$methoxy = factor(samplemeta$Genotype=="MX04+", levels=c(T,F), labels = c("MX04+", "MX04-"))

samplemeta$Treatment = as.factor(samplemeta$Treatment)
samplemeta$Treatment = relevel(samplemeta$Treatment, ref="Ctrl")
samplemeta$Mouse_ID = as.factor(samplemeta$Mouse_ID)
samplemeta$Sex = as.factor(samplemeta$Sex)
samplemeta$Brain_region = as.factor(samplemeta$Brain_region)
samplemeta$Celltype = as.factor(samplemeta$Celltype)

nCells=nrow(samplemeta)
nMice=nlevels(samplemeta$Mouse_ID)
nCelltypes=nlevels(samplemeta$Celltype)

Sample descriptive:

Data contains a total of 649 Cells from 9. Q: Original raw datset containing only frankfurt data included 1149 cells. What were the filter criteria in the primary cell type analysis

Cells per Mouse

Cells per Strain

table(Mouse_ID=samplemeta$Mouse_ID) %>% as.data.frame() %>% display_tab()

Cells per Genotype

table(Genotype=samplemeta$Genotype_corr, Treatment=samplemeta$Treatment) %>% as.data.frame() %>% display_tab()
table(Genotype=samplemeta$Genotype_corr, Celltype=samplemeta$Celltype) %>% 
  as.data.frame() %>% display_tab()
table(Genotype=samplemeta$Genotype_corr, 
      Celltype=samplemeta$Celltype, Treatment=samplemeta$Treatment
      ) %>% 
  as.data.frame() %>% display_tab()
variables=c("Celltype","Sex", "Genotype_corr", "Treatment","Phase", "Brain_region","nCount_RNA","pseudoaligned_reads", "percent.mito", "percent.ribo", "Mouse_ID")

Descriptive stats across Cell type

res = compareGroups(Celltype~., data = samplemeta[,variables], max.ylev = 10)
#summary(res)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)
Summary descriptives table by groups of `Celltype’
T/NK Microglia_0 Microglia_1 Microglia_2 Microglia_3 Microglia_4 Microglia_5 Granulocytes p.overall
N=22 N=186 N=164 N=107 N=99 N=26 N=23 N=22
Sex: .
f 6 (27.3%) 27 (14.5%) 21 (12.8%) 11 (10.3%) 11 (11.1%) 1 (3.85%) 2 (8.70%) 8 (36.4%)
m 16 (72.7%) 159 (85.5%) 143 (87.2%) 96 (89.7%) 88 (88.9%) 25 (96.2%) 21 (91.3%) 14 (63.6%)
Genotype_corr: <0.001
WT 3 (13.6%) 66 (35.5%) 93 (56.7%) 51 (47.7%) 44 (44.4%) 15 (57.7%) 11 (47.8%) 6 (27.3%)
APPPS1+ 19 (86.4%) 120 (64.5%) 71 (43.3%) 56 (52.3%) 55 (55.6%) 11 (42.3%) 12 (52.2%) 16 (72.7%)
Treatment: <0.001
Ctrl 3 (13.6%) 101 (54.3%) 113 (68.9%) 70 (65.4%) 68 (68.7%) 21 (80.8%) 12 (52.2%) 2 (9.09%)
Stroke 19 (86.4%) 85 (45.7%) 51 (31.1%) 37 (34.6%) 31 (31.3%) 5 (19.2%) 11 (47.8%) 20 (90.9%)
Phase: 0.002
G1 1 (4.55%) 79 (42.5%) 70 (42.7%) 59 (55.1%) 42 (42.4%) 12 (46.2%) 14 (60.9%) 5 (22.7%)
G2M 11 (50.0%) 50 (26.9%) 38 (23.2%) 17 (15.9%) 21 (21.2%) 6 (23.1%) 4 (17.4%) 11 (50.0%)
S 10 (45.5%) 57 (30.6%) 56 (34.1%) 31 (29.0%) 36 (36.4%) 8 (30.8%) 5 (21.7%) 6 (27.3%)
Brain_region: <0.001
Cortex 3 (13.6%) 101 (54.3%) 113 (68.9%) 70 (65.4%) 68 (68.7%) 21 (80.8%) 12 (52.2%) 2 (9.09%)
Lesion 19 (86.4%) 85 (45.7%) 51 (31.1%) 37 (34.6%) 31 (31.3%) 5 (19.2%) 11 (47.8%) 20 (90.9%)
nCount_RNA 164049 (80916) 150451 (78955) 134224 (54802) 170420 (76702) 152640 (72678) 172200 (70772) 176087 (64299) 144828 (65899) 0.002
pseudoaligned_reads 165078 (81748) 150942 (78850) 134355 (54810) 170750 (76639) 152790 (72668) 172433 (70717) 176390 (64225) 145316 (65972) 0.002
percent.mito 2.23 (0.90) 1.75 (1.17) 1.62 (1.01) 2.07 (1.07) 1.72 (1.15) 1.97 (1.02) 2.11 (0.77) 0.92 (1.00) <0.001
percent.ribo 6.53 (2.72) 2.68 (1.58) 2.75 (1.81) 2.44 (1.24) 3.28 (1.67) 2.11 (0.85) 2.91 (1.35) 2.01 (1.17) <0.001
Mouse_ID: .
23#15773 0 (0.00%) 7 (3.76%) 25 (15.2%) 12 (11.2%) 17 (17.2%) 4 (15.4%) 1 (4.35%) 0 (0.00%)
23#15774 0 (0.00%) 1 (0.54%) 30 (18.3%) 10 (9.35%) 6 (6.06%) 1 (3.85%) 1 (4.35%) 0 (0.00%)
23#15792 1 (4.55%) 11 (5.91%) 13 (7.93%) 9 (8.41%) 6 (6.06%) 6 (23.1%) 1 (4.35%) 0 (0.00%)
386 1 (4.55%) 14 (7.53%) 9 (5.49%) 2 (1.87%) 6 (6.06%) 1 (3.85%) 5 (21.7%) 3 (13.6%)
387 11 (50.0%) 10 (5.38%) 5 (3.05%) 6 (5.61%) 5 (5.05%) 0 (0.00%) 1 (4.35%) 6 (27.3%)
388 1 (4.55%) 41 (22.0%) 21 (12.8%) 18 (16.8%) 21 (21.2%) 5 (19.2%) 6 (26.1%) 0 (0.00%)
409 1 (4.55%) 33 (17.7%) 16 (9.76%) 18 (16.8%) 9 (9.09%) 3 (11.5%) 3 (13.0%) 3 (13.6%)
457 1 (4.55%) 41 (22.0%) 24 (14.6%) 21 (19.6%) 18 (18.2%) 5 (19.2%) 3 (13.0%) 2 (9.09%)
461 6 (27.3%) 28 (15.1%) 21 (12.8%) 11 (10.3%) 11 (11.1%) 1 (3.85%) 2 (8.70%) 8 (36.4%)
export2xls(export_table,paste0(home,"/docs/Descriptives.xlsx"))

download data as excel file here

#get normalized counts 
# question to Desiree hat the Seurat object been initialized with normalized data?
counts <- samples.integrated@assays$RNA@counts %>% as.data.frame()

# drop no variance data and sort by samplemeta
counts <- counts[apply(counts,1, sd) > 0, rownames(samplemeta)]

# drop genes with low detection rate (more than 5 counts per cell)
counts_per_celltype=apply(counts, 1, function(x){tapply(x, samplemeta$Celltype, function(z){sum(z>5,na.rm=T)})})

# keep RNAs with at least 10 cells with goood expression
idx=which(colSums(counts_per_celltype)>10)

counts = counts[idx,]

counts table

counts %>% display_tab()
Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.html

Hierarchical clustering

to check where the variance in the data comes from

log2_cpm = log2(counts+1)

varsset=apply(log2_cpm, 1, var)

cpm.sel.trans = t(log2_cpm[order(varsset,decreasing = T)[1:2000],])

distance = dist(cpm.sel.trans)

sampleDistMatrix <- as.matrix(distance)

#colors for plotting heatmap
colors <- rev(colorRampPalette(brewer.pal(9, "Spectral"))(255))
colors=jetcolors(255)
colors=viridis(255)

cellcol = Dark8[1:nlevels(samplemeta$Celltype)]
names(cellcol) = levels(samplemeta$Celltype)

genotypecol = brewer.pal(4,"Accent")[c(1:nlevels(samplemeta$Genotype_corr))]
names(genotypecol) = levels(samplemeta$Genotype_corr)

strokecol = brewer.pal(5,"Set2")[1:nlevels(samplemeta$Treatment)+2]
names(strokecol) = levels(samplemeta$Treatment)

mousecol = brewer.pal(9,"Set1")[1:nlevels(samplemeta$Mouse_ID)]
names(mousecol) = levels(samplemeta$Mouse_ID)

braincol = brewer.pal(3,"Set2")[1:nlevels(samplemeta$Brain_region)]
names(braincol) = levels(samplemeta$Brain_region)

ann_colors = list(
  Genotype_corr = genotypecol, 
  Mouse_ID = mousecol,
  Brain_region = braincol,
  Celltype=cellcol,
  Treatment=strokecol
)

labels = samplemeta[,c("Genotype_corr","Mouse_ID", "Brain_region", "Celltype", "Treatment")] %>%  
  mutate_all(as.character) %>% as.data.frame()

rownames(labels)=rownames(samplemeta)

pheatmap(sampleDistMatrix,
         clustering_distance_rows = distance,
         clustering_distance_cols = distance,
         clustering_method = "ward.D2",
         scale ="none",
         show_rownames=F, show_colnames = F,
         legend=T,
         border_color = NA, 
         annotation_row = labels,
         annotation_col = labels,
         annotation_colors = ann_colors,
         col = colors, 
         main = "D62 Distances normalized log2 counts")

Genotype X Treatment Statistical modelling

getres=function(Celltype="Specify", 
                Hypothesis="~1+Genotype_corr*Treatment", 
                Target="Genotype_corrAPPPS1+:TreatmentStroke",
                Randomeffect="Sex"){
  
  res= comparison_rand(designform =Hypothesis, 
                       randomeffect = Randomeffect, 
                       Samples = samplemeta$Celltype==Celltype, 
                       log_cpm = log2_cpm, 
                       samplesdata = samplemeta,
                       target=Target)
  
  
  
  labels = samplemeta[samplemeta$Celltype==Celltype,c("Treatment", "Genotype_corr","Mouse_ID", "Brain_region", "Celltype")] %>%  
    mutate_all(as.character) %>% as.data.frame()
  
  labels=labels %>% arrange(Treatment,Genotype_corr)
  plotdata=log2_cpm[res$adj.P.Val<0.05,rownames(labels)]
  
  pheatmap(plotdata,
           #clustering_method = "ward.D2",
           cluster_cols = F,
           cluster_rows  = T,
           scale ="column",
           show_rownames=F, show_colnames = F,
           legend=T,
           border_color = NA, 
           #annotation_row = labels,
           annotation_col = labels,
           annotation_colors = ann_colors,
           col = colors, 
           main = "D62 Distances normalized log2 counts")
  return(res)
  
}

generate_output=function(CT="Celltype", ...){
  respath=paste0(home, "/docs/LMER_",CT,".xlsx")
  
  analysis = getres(Celltype = CT)
  analysis_sig = analysis[analysis$adj.P.Val<0.05,]
  analysis_sig %>% display_tab()
  
  write.xlsx2(analysis_sig, file =respath , sheetName = "significant genes")
  
  ResGO = getGOresults(rownames(analysis_sig),
                       rownames(analysis),
                       "mmusculus")
  
  if(length(ResGO)>0){
    p=gostplot(ResGO)
  } else{
    ResGO=data.frame(result="no significant enrichment identified")
    p="no significant enrichment identified"
  }
  
  write.xlsx2(ResGO$result, file = respath, sheetName = "GO_enrichment", append=T)
  return(list(results=analysis,results_sig=analysis_sig, plot=p))
}

Microglia_0

res_output=generate_output("Microglia_0")

res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]

get results table here

Microglia_1

res_output=generate_output("Microglia_1")

res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]

get results table here

Microglia_2

res_output=generate_output("Microglia_2")

res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]

get results table here

Microglia_3

res_output=generate_output("Microglia_3")

[1] "no significant GO terms identified"
res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]
[1] "no significant enrichment identified"

get results table here

Microglia_4

calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference

idx=samplemeta$Celltype=="Microglia_4"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
        
         WT APPPS1+
  Ctrl   11      10
  Stroke  4       1
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()

    Fisher's Exact Test for Count Data

data:  .
p-value = 0.3562
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.005090941 3.588141911
sample estimates:
odds ratio 
 0.2877466 
#res_output=generate_output("Microglia_4")
#res_output[["results_sig"]] %>% display_tab()
#res_output[["plot"]]

get results table here

Microglia_5

res_output=generate_output("Microglia_5")

[1] "no significant GO terms identified"
res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]
[1] "no significant enrichment identified"

get results table here

Granulozytes

calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference

idx=samplemeta$Celltype=="Granulocytes"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
        
         WT APPPS1+
  Ctrl    0       2
  Stroke  6      14
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()

    Fisher's Exact Test for Count Data

data:  .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  0.0000 14.7518
sample estimates:
odds ratio 
         0 
# res_output=generate_output("Granulocytes ")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]

get results table here

T_NK

idx=samplemeta$Celltype=="T/NK"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
        
         WT APPPS1+
  Ctrl    1       2
  Stroke  2      17
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()

    Fisher's Exact Test for Count Data

data:  .
p-value = 0.3708
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   0.0481749 117.2316789
sample estimates:
odds ratio 
  3.869104 
# res_output=generate_output("T/NK")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]

get results table here

Gene expression plot


sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=German_Germany.1252  LC_CTYPE=German_Germany.1252    LC_MONETARY=German_Germany.1252 LC_NUMERIC=C                    LC_TIME=German_Germany.1252    

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gprofiler2_0.2.1            lm.beta_1.5-1               pheatmap_1.0.12             RColorBrewer_1.1-2          kableExtra_1.3.4            DT_0.21                     viridis_0.6.2               viridisLite_0.4.0           xlsx_0.6.5                  brms_2.16.3                 Rcpp_1.0.8                  compareGroups_4.5.1         data.table_1.14.2           SingleR_1.8.1               SeuratObject_4.0.4          Seurat_4.1.0                forcats_0.5.1               stringr_1.4.0               dplyr_1.0.8                 purrr_0.3.4                 readr_2.1.2                 tidyr_1.2.0                 tibble_3.1.6                ggplot2_3.3.5               tidyverse_1.3.1             DESeq2_1.34.0               SummarizedExperiment_1.24.0 Biobase_2.54.0              MatrixGenerics_1.6.0        matrixStats_0.61.0          GenomicRanges_1.46.1        GenomeInfoDb_1.30.1         IRanges_2.28.0              S4Vectors_0.32.3            BiocGenerics_0.40.0         limma_3.50.1                workflowr_1.7.0            

loaded via a namespace (and not attached):
  [1] scattermore_0.8           coda_0.19-4               bit64_4.0.5               knitr_1.37                dygraphs_1.1.1.6          irlba_2.3.5               DelayedArray_0.20.0       inline_0.3.19             rpart_4.1.16              KEGGREST_1.34.0           RCurl_1.98-1.6            generics_0.1.2            ScaledMatrix_1.2.0        callr_3.7.0               cowplot_1.1.1             RSQLite_2.2.10            mice_3.14.0               RANN_2.6.1                future_1.24.0             chron_2.3-56              bit_4.0.4                 tzdb_0.2.0                spatstat.data_2.1-2       webshot_0.5.2             xml2_1.3.3                lubridate_1.8.0           httpuv_1.6.5              StanHeaders_2.21.0-7      assertthat_0.2.1          xfun_0.30                 rJava_1.0-6               hms_1.1.1                 jquerylib_0.1.4           bayesplot_1.9.0           evaluate_0.15             promises_1.2.0.1          fansi_1.0.2               dbplyr_2.1.1              readxl_1.3.1              igraph_1.2.11             DBI_1.1.2                 geneplotter_1.72.0        Rsolnp_1.16               htmlwidgets_1.5.4         tensorA_0.36.2            spatstat.geom_2.3-2       ellipsis_0.3.2            crosstalk_1.2.0           backports_1.4.1           markdown_1.1              annotate_1.72.0           RcppParallel_5.1.5        deldir_1.0-6              sparseMatrixStats_1.6.0   vctrs_0.3.8               ROCR_1.0-11               abind_1.4-5               cachem_1.0.6              withr_2.5.0               packrat_0.7.0             HardyWeinberg_1.7.4       checkmate_2.0.0           sctransform_0.3.3         prettyunits_1.1.1         xts_0.12.1                goftest_1.2-3             svglite_2.1.0             cluster_2.1.2             lazyeval_0.2.2            crayon_1.5.0              genefilter_1.76.0         pkgconfig_2.0.3           nlme_3.1-155              nnet_7.3-17               rlang_1.0.2               globals_0.14.0            lifecycle_1.0.1           miniUI_0.1.1.1            colourpicker_1.1.1        modelr_0.1.8              rsvd_1.0.5                distributional_0.3.0      cellranger_1.1.0          rprojroot_2.0.2           polyclip_1.10-0           lmtest_0.9-39             flextable_0.7.0           Matrix_1.4-0              loo_2.5.0                 zoo_1.8-9                 reprex_2.0.1              base64enc_0.1-3           whisker_0.4               ggridges_0.5.3            processx_3.5.2            png_0.1-7                 bitops_1.0-7              getPass_0.2-2             KernSmooth_2.23-20        Biostrings_2.62.0         blob_1.2.2                DelayedMatrixStats_1.16.0 parallelly_1.30.0         spatstat.random_2.1-0     shinystan_2.6.0           beachmat_2.10.0           scales_1.1.1              memoise_2.0.1             magrittr_2.0.2            plyr_1.8.6                ica_1.0-2                 threejs_0.3.3             zlibbioc_1.40.0           compiler_4.1.2            rstantools_2.1.1          fitdistrplus_1.1-8        cli_3.2.0                 XVector_0.34.0            listenv_0.8.0             Brobdingnag_1.2-7         patchwork_1.1.1           pbapply_1.5-0             ps_1.6.0                  MASS_7.3-55               mgcv_1.8-39               tidyselect_1.1.2          stringi_1.7.6             highr_0.9                 yaml_2.3.5                askpass_1.1               BiocSingular_1.10.0       locfit_1.5-9.5            bridgesampling_1.1-2      ggrepel_0.9.1             grid_4.1.2                sass_0.4.0                tools_4.1.2               future.apply_1.8.1        parallel_4.1.2            rstudioapi_0.13           uuid_1.0-3                git2r_0.29.0              gridExtra_2.3             farver_2.1.0              posterior_1.2.1           Rtsne_0.15                BiocManager_1.30.16       digest_0.6.29             shiny_1.7.1               broom_0.7.12              later_1.3.0               writexl_1.4.0             RcppAnnoy_0.0.19          httr_1.4.2                gdtools_0.2.4             AnnotationDbi_1.56.2      rsconnect_0.8.25          colorspace_2.0-3          rvest_1.0.2               XML_3.99-0.9              fs_1.5.2                  tensor_1.5                reticulate_1.24           truncnorm_1.0-8           splines_4.1.2             statmod_1.4.36            uwot_0.1.11               spatstat.utils_2.3-0      xlsxjars_0.6.1            shinythemes_1.2.0         plotly_4.10.0             systemfonts_1.0.4         xtable_1.8-4              jsonlite_1.8.0            rstan_2.21.3              R6_2.5.1                  pillar_1.7.0              htmltools_0.5.2           mime_0.12                 glue_1.6.2                fastmap_1.1.0             BiocParallel_1.28.3       BiocNeighbors_1.12.0      codetools_0.2-18          pkgbuild_1.3.1            mvtnorm_1.1-3             utf8_1.2.2                lattice_0.20-45           bslib_0.3.1               spatstat.sparse_2.1-0     curl_4.3.2                leiden_0.3.9              gtools_3.9.2              officer_0.4.1             openssl_2.0.0             shinyjs_2.1.0             zip_2.2.0                 survival_3.3-1            rmarkdown_2.13            munsell_0.5.0             GenomeInfoDbData_1.2.7    haven_2.4.3               reshape2_1.4.4            gtable_0.3.0              spatstat.core_2.4-0